numpy.delete#

numpy.delete(arr, obj, axis=None)[source]#

Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by arr[obj].

Parameters

arrarray_like: Input array.
objslice, int or array of ints: Indicate indices of sub-arrays to remove along the specified axis.

Changed in version 1.19.0: Boolean indices are now treated as a mask of elements to remove, rather than being cast to the integers 0 and 1.
axisint, optional: The axis along which to delete the subarray defined by obj. If axis is None, obj is applied to the flattened array.

Returns

outndarray: A copy of arr with the elements specified by obj removed. Note that delete does not occur in-place. If axis is None, out is a flattened array.

See also

insert: Insert elements into an array.
append: Append elements at the end of an array.

Notes

Often it is preferable to use a boolean mask. For example:

>>> arr = np.arange(12) + 1
>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]

Is equivalent to np.delete(arr, [0,2,4], axis=0), but allows further use of mask.

Examples

>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])
>>> np.delete(arr, 1, 0)
array([[ 1,  2,  3,  4],
       [ 9, 10, 11, 12]])

>>> np.delete(arr, np.s_[::2], 1)
array([[ 2,  4],
       [ 6,  8],
       [10, 12]])
>>> np.delete(arr, [1,3,5], None)
array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])

numpy.repeat

numpy.insert